Is the NFL Fumbling Big Data Analytics?

The world of big data analytics first enters the public mainstream with the popular book and hit film Moneyball, which chronicles the Oakland A’s success using statistics and data mining to win Major League Baseball games.

But, the NFL may be ignoring the benefits of big data analytics at its own peril, according to a Ted Sundquist, former general manager of the Denver Broncos.

While so much of today’s game has been boosted by technological advances – such as film being replaced by digital video and game planning that can be done via software – many teams have not fully embraced the power of analytics to enhance their scouting, Sundquist argues.

“Most followers and fans of the NFL would be surprised at how much of the information gathered or directly purchased doesn’t go into the thought process of player selection,” he noted. “Too often emotion becomes the focal point, front and center in building an NFL roster. But taking into account tendencies, trends, and correlations from an unbiased view . . . you can gain a greater, more accurate indication of what consistently leads to winning in professional football.”

ESPN cites a clear example during Week 3 of the current NFL season of what can happen when a team ignores statistical analysis. In the final play of the game on September 22, the Detroit Lions try to draw the opposing Tennessee Titans offside instead of going for a first down. The Lions lose the game by three points.

ESPN’s Dean Oliver has analyzed data from every game since 2001 and he’s discovered 200 similar situations. Oliver finds that when teams are facing a fourth down with one yard to go between the opponent’s four- and 10-yard lines, the teams that attempt the field goal almost always tie the game.

“But historical evidence suggests the Lions had about a 38 percent chance of winning,” ESPN notes. “The Titans, after all, would have been getting the ball back in a game that they had scored two special teams touchdowns and two more on big plays in their passing game. Over the same period, teams that have gone for it on fourth-and-one went on to score touchdowns 54 percent of the time and field goals another eight percent of the time.”

Analytics-driven scouting has driven some of the most consistent gains in sports analytics, since the greatest contributor to team performance is the aggregation of star talent. To understand who is truly talented, or has the potential to become talented, teams have to look for both KPI’s and underlying metrics that are indicative of future success. In football, for instance, results-oriented metrics like sacks and interceptions might be the most attention-getting stats, but teams should be looking at leading indicators such as a defensive player’s ability to cross the line of scrimmage, number of passes challenged, or cutting off yards after a catch to see if there is the potential for a matchup challenge (or a market inefficiency) that the traditional numbers don’t show.

When I started doing this in baseball in the mid-90s, it was fascinating to see how hitters profiled once park effects and defense were taken into account. Those of us who were able to judge power and batting eye accurately had a big advantage in scouting over everyone else on a year-to-year basis.